{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5e3efaca-e25b-43e2-ab33-47802eb42b47",
   "metadata": {},
   "outputs": [],
   "source": [
    "# file name\n",
    "file = '1.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2a961024-d940-49da-8079-13f31f7021d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load File\n",
    "import pandas as pd\n",
    "df = pd.read_csv(file, header = None, names=[\"A\", \"K\", \"Q\", \"J\", \"T\", \"9\", \"8\", \"7\", \"6\", \"5\", \"4\", \"3\", \"2\"])\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Displaying dataframe as an heatmap\n",
    "# with diverging colourmap as Greys\n",
    "plt.imshow(df, cmap=\"Greys\") \n",
    "\n",
    "# Displaying a color bar to understand\n",
    "# which color represents which range of data\n",
    "plt.colorbar()\n",
    "\n",
    "# ticks style\n",
    "plt.rcParams['xtick.top'] = plt.rcParams['xtick.labeltop'] = True\n",
    "plt.rcParams['xtick.bottom'] = plt.rcParams['xtick.labelbottom'] = False\n",
    "\n",
    "plt.rcParams['ytick.right'] = plt.rcParams['ytick.labelright'] = False\n",
    "plt.rcParams['ytick.left'] = plt.rcParams['ytick.labelleft'] = True\n",
    "\n",
    "# title\n",
    "#plt.xlabel(\"Hand Strength Adjustment metrix\")\n",
    "\n",
    "# Assigning labels of x-axis \n",
    "# according to dataframe\n",
    "plt.xticks(range(len(df)), df.columns)\n",
    "\n",
    "# Assigning labels of y-axis \n",
    "# according to dataframe\n",
    "plt.yticks(range(len(df)), [\"A\", \"K\", \"Q\", \"J\", \"T\", \"9\", \"8\", \"7\", \"6\", \"5\", \"4\", \"3\", \"2\"])\n",
    "\n",
    "\n",
    "# Displaying the figure\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.6"
  }
 },
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